The present invention relates to a fingerprint classification system used for automatic classification of fingerprint images.
Fingerprint verification systems are widely used for personal identification. Among them, large systems as that operating in a Police Office are generally provided with a database that has registered within it a large number of fingerprint images classified and sorted to be retrieved efficiently. However, the classification of the large number of fingerprint images involves a large amount of manpower.
Therefore, an automatic fingerprint classification is greatly desired.
As for different methods of fingerprint classification, there is known a method of classification by analyzing features and characteristic lines extracted from thinned images of fingerprints, of which an example is described in a U.S. patent application No. 08/690,744, filed on Aug. 1, 1996, and entitled "Skin Pattern and Fingerprint Classification System", (hereafter to be called the first document), a classification method according to ridge line features of fingerprints, which is disclosed in Japanese patent applications laid open as Provisional Publications No. 52974/'92 and No. 52975/'92 (hereafter called the second and the third documents), or a method of classification making use of a neural network according to a feature vector obtained from fingerprint ridge directions, which is described in "Massively Parallel Neural Network Fingerprint Classification System" by C. L. Wilson et al., National Institute of Standards and Technology, NISTIR 4880, 1992, (hereafter called the fourth document).
In these methods, fingerprints are classified into categories such as the whorl, the loop, the arch and so on.
Each of these fingerprint classification system has its own merits and demerits. For example, the classification method of the first document is sensitive even to a local blot of degraded fingerprint images because it classifies fingerprints according to minutiae of their thinned line images, but has an algorithm that is very insensitive to shifting or rotation of the fingerprint images.
On the contrary, the fingerprint classification method by way of neural network of the fourth document by C. L. Wilson et al. has an algorithm that is insensitive to noises such as local stains or blots, since it classifies fingerprint images making use of features of a whole direction pattern, while it is easy to mis-classify fingerprint images somewhat rotated or shifted.
As described above, different classification methods have different classification characteristics. Therefore, a higher classification performance can be expected by integrating these different fingerprint classification methods.
As for the fingerprint classification system by way of a neural network, the more teaching data sets should bring the higher classification performance. There are cases, however, that even if a large number of teaching data sets can be prepared, it can not learn all of them at once because of its memory limit or restriction of its algorithm.
Also in these cases, the classification precision can be improved by the integration, making use of the large number of teaching data sets as follows. By dividing the large number of teaching data sets into several groups, the data learning is performed for each of the divided groups to obtain each corresponding parameter set. Then, the same unknown fingerprint images are classified by the neural network applying each of the parameter sets. By integrating the classification results thus obtained, a higher precision classification can be expected.
As a method for providing a higher classification performance from classification results obtained from different classification systems or the same classification system with different parameters, it is easily considered to elect a category of a fingerprint to be finally output by a majority from categories such as the arch or the loop indicated by each classification result. For example, in a case where there are provided three different classification systems or a classification system provided with three different parameter sets for classifying a fingerprint image into one of five categories, the plain arch A, the tented arch T, the left loop L, the right loop R and the whorl W, a category indicated by more than one of their three classification results is elected as the final category of the fingerprint image. When the three classification results conflict with each other, the classification result of a system or a parameter set which has shown the most reliable performance is elected, or all of them are rejected, if the rejection is allowed.
Thus, a higher classification precision can be obtained by the integration than each individual classification.
However, with such a simple selection by majority as described above, improvement of classification performance is limited and it is difficult to modify its rejection level, for example, because there is but little variety in the method.